Edge Computing: The Magic Spell to Bring Homes and Industries to Life with IoT in UK

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Edge Computing: Why does it matter?

Consider the following scenario: Your alarm clock goes off while you're sleeping in your temperature-controlled bed, causing your drapes to separate, allowing just enough sunlight to gently wake you up. As soon as you get out of bed, the sound of lively music plays from your smart speakers. When sensors on your floor detect that you are awake, your coffee machine whirrs into action. You walk into the kitchen, where a pot of freshly prepared black coffee greets you, and the music is replaced with a narration of the morning's news headlines before you take your first sip.

Doesn't it sound like Tony Stark's life? But you may ask, what does it have to do with me? Well, sooner than you would imagine, you can live a hyper-advanced lifestyle.

The notion of the Internet of Things, or IoT, has been around for a while. With the aid of software, sensors & other technologies; a web of interconnected devices exchange data with one another. In areas including communication, healthcare, manufacturing, transportation, and, of course, home automation, the network has produced plenty of ground-breaking applications (smart homes). Because of its pervasiveness, there are now 620 different publicly-recorded IoT systems, up from 260 in 2015.This includes behemoths such as Microsoft & Google who are vying for a slice of the ever-growing IoT platform pie. According to estimates, worldwide investment on IoT would reach a staggering 1.1 trillion dollars by 2023. This may be explained by organizations’increased attention on security, as well as the need to improve efficiency and save operating expenses. Smart homes and smart cities are also worthwhile investments in technology.

However, there is a bottleneck in the development of IoT and other such technologies that will usher us all into the future. This bottleneck manifests itself in the processing of all data supplied by IoT devices (among other sources), which clogs up today's centralized networks. Edge Computing is a term used to describe a type of computing that takes place Edge Computing is the magic spell that will bring us closer to that wonderful, automatically made pot of coffee in the morning, with improvements in efficiency, latency, bandwidth usage, and network congestion. Not to mention the security, efficiency, and cost-cutting implications for industries.

What is Edge Computing Technology?

Edge computing is a computing paradigm that allows computation to take place close to or at the data source. This contrasts with the usual approach of using the cloud at the data center as the sole place for computing. This does not imply that the cloud will vanish. It just indicates that the cloud is approaching you.

Edge computing improves the performance of online applications by bringing processing closer to the data source. The definition of the word "edge" in this context is literal geographical dispersion. This eliminates the need for long-distance connections between clients and servers, lowering latency and bandwidth consumption.Edge computing improves Internet devices and online applications by bringing computers closer to the data source. Because of its use, the worldwide edge computing industry is expected to grow from 3.6 billion in 2017 to 15.7 billion in 2025.

For an even simpler breakdown of Edge Computing &its concepts, take a look at the 2-parter STL TechTalk from industry expert himself Mr. Sandeep Dhingra:

Part 1: https://www.youtube.com/watch?v=GhmpzHUrnso (Embed this in content)

Part 2: https://www.youtube.com/watch?v=aBiyxorYYNc (Embed this in content)

Why Is Edge Computing Important?

The expansion of connected devices such as smartphones, tablets, and gadgets, as well as the recent surge in online content consumption, may eventually overwhelm today's centralized networks. According to IDC, there will be 55.7 billion connected devices worldwide by 2025, with 75% of them connected to an IoT platform. According to IDC forecasts, linked IoT devices would create up to 73.1 ZB data by 2025. (up from 18.3 ZB in 2019). While video surveillance and security will account for most of this data, industrial IoT applications will also contribute significantly. With such a surge in data, today's centralized networks may soon become overburdened with traffic. Edge computing attempts to fight the inevitable data surge with a dispersed IT architecture that moves data center investments to the network periphery.

How does Edge Computing work?

Our lives have been entirely taken over by what we've come to know as cloud computing services, whether it's email services like Gmail or online picture storage services like iCloud. Even major corporations have begun to migrate their essential applications to the cloud, including data from thousands of sensors installed within their production units, to gain rapid insights and the capacity to remotely monitor their equipment.

However, as these Internet of Things sensors gain traction and fuel rapid data growth, they place enormous strain on central cloud servers and begin to suffocate network capacity.

To address these issues, edge computing brings computers closer to these IoT devices. You may think of them to process your video modifications on your iPhone rather than transmitting them to a central server. Furthermore, computing demands are migrating closer to where the device is or where the data is consumed, necessitating data processing to move closer to the devices as well.

To create an edge cloud, operators establish multiple of these edge data centers rather than a single cloud data center. Edge cloud enables numerous new applications by bringing computation closer to the devices.

Edge computing appears to be the ideal answer since it processes data and even analytics at or near the original data source, lowering latency, lowering bandwidth costs, and making the edge network less prone to a single point of failure.

Analysts predict that by 2025, there will be 30.9 billion IoT devices on the market, up from 13.8 billion in 2021. Increases in IoT devices will place enormous strain on cloud data centers, necessitating the use of edge IoT to keep up with the demand.

What Are the Key Benefits of Edge Computing?

Edge computing topology can help with latency for time-sensitive applications, IoT efficiency in low bandwidth situations and overall network congestion to solve network challenges.

  • Latency: The time-to-action is decreased when data processing takes place locally rather than at a faraway data center or cloud because of the physical closeness. Because data processing and storage will take place at or near edge devices, IoT and mobile endpoints will respond to vital information in near real time.
  • Congestion: Edge computing will assist the wide-area network cope with the increased traffic. By decreasing the amount of bandwidth, you use, you will be able to save time and money. This is a significant barrier in the age of mobile computing and the Internet of Things. Rather of overwhelming the network with relatively unimportant raw data, edge devices will process, filter, and compress data locally.
  • Bandwidth: The edge computing infrastructure will enable IoT devices in settings where network connection is unstable. Such environments include offshore oil rigs, distant power facilities, and remote military installations. Even if the cloud connectivity is irregular, local compute and storage resources will allow for ongoing operation.

How does 5G &edge computing work together?

5G is unlikely to make a big influence on most of our lives. 4G speeds are sufficient to handle the most demanding data speed requirements of nearly any application now available. However, 5G is crucial because of the new possibilities it opens up, such as autonomous drones, remote telesurgery, and autonomous driving, to name a few.

Although 5G has the potential to provide speeds up to ten times faster than 4G, the user experience may not always be as envisioned. That's because, despite the network's high speeds, latency can negate many of the advantages.

However, without 5G speeds and coverage, software developers for these novel use cases will have little motivation to roll out these services, which have only been tested with a 4G-edge computing combo so far.

Edge computing and 5G combine to provide a larger, quicker conduit with a shorter distance for data to travel, making the two not only complimentary but also interdependent.

Now that we've established the significance of Edge Computing for the future of data processing, it's time to clarify where STL Tech fits into this larger picture.

What is STL’s Role in the World of Edge Computing?

STL Tech – Sterlite Technologies Limited has a presence in edge cloud infrastructure and multi-cloud platforms when it comes to Edge Computing. STL Tech developed a cloud-native software stack based on a disaggregated and programmable micro-service architecture only last year. This technique separates software from the hardware layer, allowing virtual networks with open interfaces to be created. This has the benefit of significantly decreasing the time it takes for new digital services to reach the market. Edge computing is also given a push by broadband network disaggregation and central office re-architecture, which reshapes the way we operate.

These efforts have been recognized by STL Partners – a research and consulting firm – in their recently concluded competition titled “Edge companies to watch in 2021” whose purpose is to draw attention to companies that are deemed to be at the cutting-edge of, well, edge technology. STL is truly honoured to have been featured in the list of Top 60 Companies & is even more motivated to continue pushing boundaries in the edge computing space for years to come.

Ambitions for 2021

STL Tech intends to provide multi-access edge apps as well as a multi-access convergent platform for wireless and wireline focused edge computing.

This, of course, folds neatly into STL Tech’s greater commitment to helping large citizen networks, enterprises, telcos & cloud companies present state-of-the-art experiences to their customers. Our company's ambition continues to change everyday life by using technology to create next-generation interconnected experiences.

FAQs

How edge computing compares with cloud computing?



To understand the distinction between cloud and edge computing, one must first grasp how data is processed in each network architecture.

Most of the data processing in IoT devices is now done in the cloud, on a network of centralized servers. As a result, for data aggregation and low-level processing, all low-end apps, as well as gateway devices, are employed.

Edge computing differs from traditional computing in that it takes a completely new approach. It moves processing away from centralized servers and closer to the end-users. Around 45 percent of the world's data will be stored and processed near the network's edge, or even closer, by 2020.

What are certain limitations of Cloud Computing?

Cloud computing will not be able to keep up with the current data consumption surge. Two difficulties emerge throughout the processing step. Processing delay and a large amount of idle resources are two of these issues. These issues affect decentralized data centers, mobile edge nodes, and cloudlets alike.

Everything is heaped on and transmitted to the cloud for additional processing when linked devices create data. As a result, data centers and networks in the cloud are overburdened, resulting in increased latency and network inefficiencies.

Data may be processed closer to the source of the data with edge computing. This method not only helps to decrease data reliance on the app or service, but it also speeds up data processing.

What Are the Usesof Edge Computing?

Virtualized Radio Networks and 5G (vRAN)

Operators are increasingly virtualizing sections of mobile networks (vRAN). Both in terms of cost and variety, this is advantageous. Modern virtualized RAN hardware is intended to do complex processing with minimal latency. Operators would anticipate edge servers to allow them to virtualize their RAN near to the cell tower.

Predictive Maintenance

Manufacturing businesses would like to be able to proactively identify and assess the health of their machines in the production line before they fail. The advantage of edge computing is that it allows data processing and storage to be brought closer to the equipment. IoT sensors can track machine health and conduct real-time analytics with little latency.

Content Delivery

Material distribution may be greatly improved by caching content at the edge, such as music, video streams, and web pages. Latency may be significantly reduced. The goal of content providers is to expand content delivery networks to the edge, providing network stability and customization based on user traffic demands.

Traffic Management

Cities may benefit from edge computing to better manage traffic. Edge computing eliminates the need to send large amounts of data to a centralized cloud, reducing bandwidth and latency expenses. Managing autonomous automobile movements, regulating the opening and closure of extra lanes, and optimizing bus frequency in response to demand fluctuation are all instances of this.

What Edge Computing Trends to Look out for in 2021?

As with most things in technology, we can expect rapid advancements in the field of Edge Computing to continue this year as well. Some of the major trends to look out for are:

COVID-19 to Accelerate Innovation

The pandemic has accelerated technology advancements in every visible sector, including Edge Computing. Innovators will most likely focus on generating actionable insights from raw data gathered from IoT technologies such as cleaning, sanitation, thermal imaging, and social distancing in 2021.

Partnerships Between Cloud and Edge Providers

Edge data will be linked with cloud-based applications soon. This will need the formation of agreements between cloud and edge providers. The concept is for the cloud to provide big data processing while the edge supports immediacy.

Combination of Edge with Machine Learning and Artificial Intelligence

Until recently, the growing complexity of data solutions has posed problems in terms of data pre-processing using near-edge technology. With the aid of container-packaged analytics apps, open standards, and AI/ML-optimized hardware, data analytics at the edge and on-device ML are now conceivable. Real-time customization and speedier decision-making will be possible with them.

What are certain drawbacks of edge computing?

Edge computing, like any other technology, has its own set of downsides. The first consideration is the cost. When compared to a central data center that manages all of your computing needs on its own, edge cloud necessitates the deployment of a large number of edge nodes or edge devices, each with its own computation and storage capabilities to execute local processing. Costs may rise because of this.

Edge computing's second key issue, rather than a disadvantage, is security. Edge nodes and edge devices are often tiny computers with limited processing capabilities, and as a result, they are not always provided with the same level of security as a central data center. This is complicated further by the fact that each edge device supports varying degrees of authentication and security mechanisms, necessitating careful attention to the edge cloud's security architecture to avoid any breaches.

What is the Network Edge?

The network edge functions as a barrier between the internet and the area where all the edge devices and edge networks are located. This critical link between the internal and external networks also serves as a security bridge, which network managers must account for while planning their edge network.

The point where the corporate network meets a third-party network is known as the network edge. This location is sometimes referred to as the internet edge or the WAN edge.

What are some more applications of edge computing?

Edge compute may be utilized for any application that demands immediate processing and reaction time, or, in more technical terms, applications that require low latency. Several applications have been proven for years but are not widely used because to the absence of low latency that has previously existed. Edge computing fills in the gaps, allowing for wider adoption of such technologies.

Consider AR/VR applications, which have been around for decades. However, to provide a lag-free experience and gain widespread acceptance, a low-latency network is required, which is made feasible by edge computing.

Remote asset monitoring in the oil and gas sector, autonomous driving, telemedicine and telesurgery, cloud gaming, and predictive maintenance are just a few examples. Edge computing has also opened a slew of new possibilities, and we may expect thousands of new applications to develop over time.

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